a non-invasive approach examining north american
TRANSCRIPT
A NON-INVASIVE APPROACH
EXAMINING NORTH AMERICAN RIVER OTTER
ABUNDANCE AND SOCIALITY
by
Kristin E. Brzeski
A Thesis
Presented to
The Faculty of Humboldt State University
In Partial Fulfillment
Of the Requirements for the Degree
Master of Science in
Natural Resources: Wildlife
November, 2010
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ABSTRACT
A Non-invasive Approach Examining North American River Otter Abundance and
Sociality
Kristin E. Brzeski
An understanding of population demographics is a key component for developing
successful wildlife conservation and management plans. However, elusive and secretive
carnivores such as river otters can be difficult to trap and observe, making investigations
of population number and social structure extremely challenging. Advances in molecular
genetics techniques have facilitated the use of non-invasive methods to examine
abundance and genetic structure of wild populations when traditional wildlife survey
methods may not be appropriate. I applied non-invasive methods to estimate abundance,
sociality and kinship of a population of North American river otters (Lontra canadensis)
inhabiting the Humboldt Bay area, California, USA. Through microsatellite multi-locus
genotyping and closed population mark/recapture modeling, I estimated abundance as 41-
51 river otters in the Humboldt Bay region. Coastal river otters associated in family
groups, with evidence for the temporary formation of male bachelor groups. There was
fine scale population structuring that was most likely a function of social family groups
and isolation by distance gene flow. With these results, we can better understand
population demographics of coastal-living river otters and thus inform future research
and conservation decisions in the Humboldt Bay region.
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ACKNOWLEDGMENTS
I would like to sincerely thank my advisor, Dr. Micaela Szykman Gunther, for her
invaluable advice, support, and mentorship throughout my tenure as a graduate student at
Humboldt State University. Without her guidance and encouragement, this thesis
research, as well as my personal growth as a wildlife biologist, would not be as strong as
it is today. This research would not have been possible without the technical support of
Mr. Anthony Baker, whom I am grateful and indebted to for his laboratory expertise and
instruction. I would also like to thank my committee members: Dr. Andrew Kinziger for
his insightful statistical advice and guidance, and Dr. Jeff Black for his valuable input and
vast knowledge of Humboldt Bay river otters. Funding for this project was provided by
Humboldt State University Sponsored Programs Foundation, Humboldt State University
Office of Research and Graduate Studies, the Eureka Rotary Club, the Richard J.
Guadagno Memorial Scholarship, the Marin Rod and Gun Club, as well as the Stanley
Harris Scholarship. Finally I’d like to thank my mother, Vicki Brzeski, my father and his
wife, Gregg Brzeski and Christine Hopkins, as well as Jared Wolfe, for their love and
unconditional support.
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TABLE OF CONTENTS
Page
ABSTRACT.......................................................................................................................iii
ACKNOWLDGMENTS.....................................................................................................iv
TABLE OF CONTENTS.....................................................................................................v
LIST OF TABLES ....................................................................................................... .....vi
LIST OF FIGURES ........................................................................................................ viii
LIST OF APPENDICES .................................................................................................... x
INTRODUCTION..............................................................................................................1
METHODS ........................................................................................................................ 5
Study Area ............................................................................................................. 5
Non-invasive field collection ................................................................................. 8
Molecular methods................................................................................................. 8
Test of assumptions.............................................................................................. 10
Abundance estimates ........................................................................................... 13
Genetic population structure and relatedness ...................................................... 15
RESULTS ........................................................................................................................ 18
Field sampling and molecular methods ............................................................... 18
Test of assumptions.............................................................................................. 18
Abundance estimates ........................................................................................... 25
Genetic population structure and relatedness ...................................................... 35
DISCUSSION .................................................................................................................. 45
LITERATURE CITED .................................................................................................... 55
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LIST OF TABLES
Table Page
1 Total number of river otter scat, jelly, and mixed scat with jelly
samples collected throughout the Humboldt Bay region, California,
USA from 18 May-31 October, 2008. Samples from north to south were
Little Rive000r, Mad River, Arcata Marsh and Wildlife Sanctuary
(AMWS), Mad River Slough (MRS), Woodley Island, Elk River,
and Humboldt Bay National Wildlife Refuge complex (HBNWR)......................19
2 Genotyping success of river otter DNA extracted from scat samples in the
Humboldt Bay region, California, USA from 18 May-31 October, 2008.
Sampling sites from north to south were Little River, Mad River, Arcata Marsh
and Wildlife Sanctuary (AMWS), Mad River Slough (MRS), Woodley Island,
Elk River, and Humboldt Bay National Wildlife Refuge complex (HBNWR).....20
3 Diet and amplification success of river otter DNA extracted from scat
samples collected in the Humboldt Bay region, California, USA from
18 May-31 October, 2008......................................................................................22
4 Sample type, [scat, jelly or mixed samples (scat and jelly)], and
genotyping success of river otter DNA extracted from
samples collected in the Humboldt Bay region, California, USA
from 18 May-31 October, 2008….........................................................................23
5 The number of alleles, observed heterozygosity (HO), expected
heterozygosity(HE), tests for conformance to Hardy-Weinberg equilibrium,
and allele sizes and frequencies of six microsatellite loci, among 41
river otters in the Humboldt Bay region, California, USA, from 18 May-31
October, 2008.........................................................................................................24
6 Repeat motifs, Probability of Identity (PID), sibling Probability of Identity
(P(ID)sibs), allelic dropout rates (ADO), and false allele rates (FA) at six loci,
among 41 river otters in the Humboldt Bay region, California, USA, from 18
May-31 October, 2008...........................................................................................26
7 Home site (location most often detected), sex, sites detected at,
and total linear distance traveled (km; sometimes on multiple trips) for
all river otters detected at multiple sites in the Humboldt Bay
region, California, USA from 18...........................................................................28
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LIST OF TABLES (CONTINUED)
8 Pairs of roaming river otters detected at the same site on the same date and
pairwise relatedness values (Queller and Goodnight’s R values from
GenAlEx) between dyads in the Humboldt Bay region, California, USA
from 18 May-31 October 2008..............................................................................29
9 Total number of male, female and unknown sex river otters (and maximum
number including roaming otter visitations) detected using non-invasive
genetic samples from the Humboldt Bay region, California, USA, from
18 May-31 October 2008.......................................................................................32
10 Closed population model rankings from program MARK for river otter
captured in August-September 2008 and only September 2008, in the
Humboldt Bay region, California, USA. Corrected Akaike Information
Criterion (AICc) was used to rank models.............................................................33
11 Population estimates and corresponding standard error (SE) and 95%
confidence intervals (CI), from top ranked closed population models for river
otters sampled from August-September, 2008, in the Humboldt Bay region,
California, USA. Estimates were evaluated for August-September combined
capture histories and September alone...................................................................34
12 The number of correct (assigned same location as sampled) and
incorrect (assigned different location than sampled) assignments based
on log-likelihood values for 41 river otter genotypes in the Humboldt Bay
region, California, USA, from 18 May-31 October, 2008.....................................38
13 Relatedness coefficients (R) between study locations (within locations on
diagonal) in the Humboldt Bay region, California, USA, calculated from river
otters non-invasively sampled 18 May-31 October 2008......................................40
14 Mean coefficient of relatedness (Queller and Goodnight’s R values from
GenAlEx) within and between study areas for river otter genotypes
detected in the Humboldt Bay region, California, USA, from 18 May-31
October 2008..........................................................................................................41
viii
LIST OF FIGURES
Figure Page
1 Focal latrine sites sampled non-invasively for river otter scat from
18 May-31 October, 2008, Humboldt Bay, California, USA..................................7
2 Agarose gel image showing results of restriction enzyme TaqαI digestion of
PCR product from ZFX/ZFY primer pairs P1-5EZ/P2-3EZ (far left) and
ZFKF 203L/AFKF 195H (far right). Primer pair 203L/AFKF 195H yielded
one X band of 153 bp and one Y band of 203 bp; molecular weight scale on
right in base pair size.............................................................................................11
3 Total number of river otter scat samples collected, total number of individuals
successfully genotyped, and newly detected genotypes in the
Humboldt Bay region, California, USA, from 18 May-31 October, 2008.
*May only sampled for 2 weeks …………...................………………........…....21
4 Probabilities of identity (PID) from river otter genotypes sampled
non-invasively from the Humboldt Bay region, California, USA, from 18
May-31 October 2008. Probabilities were calculated for six microsatellite
loci and arranged in order of increasing PID value.................................................27
5 Movement patterns for the only 8 river otters detected at multiple sites in the
Humboldt Bay region, California, USA, from 18 May-31 October, 2008.
Circles denote sampling site and lines and arrows indicate paths of
movement...............................................................................................................30
6 Bayesian Information Criterion (BIC) values for models with increasing
clusters to determine the number of genetically differentiated groups
derived from multi-locus genetic data of 41 river otters in the Humboldt Bay
region, California, USA, from 18 May-31 October 2008…..................................36
7 Discriminate Analysis of Principal Components constructed from 41 river
otter genotypes determining genetic differentiations between locations
in the Humboldt Bay region, California, USA, from 18 May-31 October 2008.
The locations from north to south were Little River, Mad River, Arcata Marsh
Wildlife Sanctuary (Marsh), Mad River Slough (MRS), Elk River, and
Humboldt Bay National Wildlife Refuge complex (HBNWR). Eigenvalues
(insert) show the majority of variance was captured within the
first and second principal component (PC)…........................................................37
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LIST OF FIGURES (CONTINUED)
8 Mean relatedness values (Queller and Goodnight’s R values from GenAlEx)
within sampling locations for (a) all river otters sampled and (b) for only river
otters that exhibited no movement between sites, in the Humboldt Bay region,
California, USA, from 18 May-31 October 2008. Sites from north to south were
Little River (Little) Mad River (Mad), Arcata Marsh and Wildlife Sanctuary
(AMWS), Mad River Slough (MRS), Elk River (Elk), and Humboldt Bay
National Wildlife Refuge (HBNWR). Upper and lower whiskers denote 95%
confidence limits determined by 999 bootstrap resampling. Female (♀), male
(♂), and unknown sex (?) sample sizes displayed above sampling sites..............42
9 Mean relatedness values (Queller and Goodnight’s R values from GenAlEx)
for male and female river otters sampled in the Humboldt Bay region,
California, USA, from 18 May-31 October 2008. Upper and lower whiskers
denote 95% confidence interval determined by 999 bootstrap resampling...........43
10 A test of isolation by distance for river otters detected at all sampling
locations (a) and all locations but Little River (b) in the Humboldt Bay
region, California, USA from 18 May-31 October 2008. Genetic distance
was measured by R values and geographic distance was measured in linear
distance (km)..........................................................................................................44
x
LIST OF APPENDICES
Appendix Page
A Universal Transverse Mercator (UTM) coordinates of river otter latrine
sites sampled 18 May-31 October 2008 in Humboldt Bay, California, USA........64
B Multi-locus microsatellite genotypes (N=41) for 6 loci and sex type
derived from DNA extracted from non-invasive scat sampling of river
otters in Humboldt Bay, California, USA, from 18 May-31 October 2008..........65
C River otters found at more than one sampling location, sex type, and dates
detected at all sites in Humboldt Bay, California, USA, from 18 May-31
October 2008. No roaming river otters were detected at Little River..................66
D Capture probability estimates (p), standard error (SE), and 95%
confidence limits (CI) estimated from closed population models as determined
by program MARK for river otters sampled in the Humboldt Bay region,
California, USA, from 1 August-30 September, 2008. The top section is for
capture histories combining August and September (9 capture occasions);
the bottom section is only for September (5 capture occasions)...........................67
1
INTRODUCTION
North American river otters (Lontra canadensis) are sensitive to anthropogenic
changes in the landscape and are therefore key indicators of intact wetland ecosystems
(Lariviere and Walton 1998, Bowyer et al. 2003). They suffer more from environmental
degradation than other mammals due to movement between terrestrial and aquatic
landscapes, inherently increasing their exposure to water pollutants and environmental
contaminants (Foster-Turley 1990, Ben-David et al. 1998, Melquist et al. 2003). The
species must contend with persistent and damaging chemical pollutants shown to
decrease fecundity and survival (Elliott et al. 2008). Future habitat alterations due to
global climate change are predicted to alter wetland hydrology potentially severely
enough to be beyond the limits of adaptation and tolerance for carnivores such as river
otters (Burkett and Kusler 2000). Monitoring river otter populations is essential for
establishing baseline data for future comparison and creating management plans to
mitigate population disturbance due to past threats.
Despite their importance as indicators of wetland health, relatively little is
understood about river otter ecology (Melquist and Dronkert 1987, Kruuk 2006).
Understanding the natural history of an animal, particularly social structure, spatial
organization, and movement, which influence gene flow, is essential to appropriate
conservation and management (Kruuk 2006, Manel et al. 2003). In general, female river
otters exhibit small, exclusive core-areas of use with overlapping home ranges outside
this core area, while males exhibit larger home ranges that overlap with both other males
and females (Kruuk 2006). However, social patterns tend to be complex and variable
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among and even within contiguous river otter populations (Melquist and Hornocker 1983,
Reid et al. 1994, Blundell et al. 2002a, Spinola 2003, Gorman et al. 2006). For example,
sociality ranges from completely solitary males and females, to social family groups, to
unrelated large-male bachelor associations (Melquist and Hornocker 1983, Spinola 2003,
Gorman et al. 2006). Group sizes vary depending on type of social group. Female
family groups may have 1-3 adult females, usually a mother with reproductive daughters
and all of their associated pups, while reported male bachelor groups can be as large as
18, though generally average 6-8 river otters (Shannon 1989, Rock et al. 1994, Blundell
et al. 2002a).
River otters adjust their spatial distribution in response to environmental
conditions and seasonal resource availability (Mason and Macdonald 1986, Blundell et
al. 2000). Accordingly, river otter gene flow should also vary by habitat conditions and
characteristics. Genetic diversity, partially maintained through dispersal and movement
between populations, is important for a species to adapt and persist in a changing
environment (Manel et al. 2003). Several comprehensive studies employing both
behavioral and genetic data for Alaskan coastal river otters found low levels of dispersal
and gene flow, as well as significant isolation by distance for male otters (Testa et al.
1994, Blundell et al. 2002a, Blundell et al. 2002b, Blundell et al. 2004). Understanding
metapopulation dynamics is necessary for population monitoring since the low dispersal
rate of river otters may make natural population growth slow and increase genetic
structuring of subpopulations (Blundell et al. 2002b). Study of coastal river otter
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demographics and genetic structure has been mostly limited to Alaska. Parallel
assessments from California would elucidate the generality of the findings.
The elusive nature of river otters makes direct observations challenging (Swimley
et al. 1998). River otters are difficult to trap and live trapping can lead to severe animal
injury (Kruuk 1995, Serfass et al. 1996, Blundell et al. 1999). Molecular genetics allow
utilization of non-invasive techniques as an alternative to traditional wildlife field
methods (Bellemain and Taberlet 2004), providing a new tool to census populations and
examine social structure and spatial interactions (Hughes 1998, Piggott and Taylor 2003,
Hung et al. 2004). Non-invasive sampling of scat, hair, mucus or any tissue left behind
by an animal can be used to establish individual identification via DNA ‘finger-printing’
methods (Gerloff et al. 1999, Taberlet and Luikart 1999). These techniques facilitate
monitoring of elusive species and eliminate the risk of animal injury (Taberlet et al. 2001,
Anderson et al. 2006, Solberg et al. 2006).
Northern California has an entirely native river otter population and, consistent
with nationwide trends, there have been relatively few river otter monitoring efforts
conducted within the state (Gould 1977, Raesly 2007, Black 2009). The Humboldt Bay
region in northern California has a rich fish and avian community, the predominant
components of river otter diets (Chamberlain and Barnhart 1993, Colwell 1994, Penland
and Black 2009). These features make it conducive to supporting stable river otter
populations. This coastal system is ideal for examining social grouping and gene flow of
river otters within a discrete wetland system and along a coastline. A citizen scientist
database and local observations have provided anecdotal evidence of female family and
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male bachelor group formation in the Humboldt Bay region (Shannon 1989, 1998, Black
2009). Utilizing non-invasive molecular techniques, I identified individual animals to
meet the following objectives:
1. Estimate abundance of male and female river otters in the Humboldt Bay region.
2. Evaluate genetic population structure and relatedness to elucidate social groups
and the presence of family groups among river otters in the Humboldt Bay region.
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METHODS
Study area
Humboldt Bay is located in northern, California (Humboldt County),
approximately 145 km south of the Oregon-California border. The Bay is California's
second largest bay (approximately 66 km2). The diversity and abundance of estuarine
organisms is greater only in San Francisco Bay (Chamberlain and Barnhart 1993).
Humboldt Bay supports a vast number and variety of fish and avian species, preferred
diet of Humboldt County river otters (Modafferi and Yocom 1980, Reeves 1988, Penland
and Black 2009). Over 30 fish species and two crab species have been detected in the
Bay (Chamberlain and Barnhart 1993), and the coast hosts naturally-spawning Pacific
salmon (Oncorhynchu spp.) and steelhead (Oncorhynchus mykiss) stocks (Nehlsen et al.
1991). The region is also a major winter and stopover site for migratory shorebirds along
the Pacific flyway (Cowell 1994). More than 200 bird species regularly feed, rest, or nest
around the Bay throughout the year.
The study area covered approximately 45 km of coastal habitat. Linear home
ranges of coastal river otters have a large degree of spatial variation, reported to range
from approximately 5-73 km in length (Mason and Macdonald 1986, Bowyer et al.
1995). Average home ranges for female and male coastal river otters range 8-20 km and
21-45 km, respectively (Bowyer et al. 1995, Blundell et al. 2001). The Humboldt Bay
study area was long enough to detect numerous individual river otter home ranges.
Although river otters have been reported to occur at a density of one adult otter per linear
km within resource rich environments (Kruuk 1995), a more common range in coastal
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regions is 26-60 animals/100 km (Testa et al. 1994, Blundell et al. 2004). The unique
attributes and resource richness of the Humboldt Bay area could support a high density of
river otters.
River otters deposit scat and mucus produced in the lower intestine (referred to as
jellies) as scent marks at terrestrial latrines sites usually within their home ranges
(Melquist and Hornocker 1983, Kruuk 1992, Ben-David et al. 1998, Lariviere and
Walton 1998, Oldham and Black 2009). Latrine sites are persistent over time, prominent
in their environment, and distinguishable from other species, easing detection and
collection of river otter feces (Modafferi and Yocom 1980, Newman and Griffin 1994,
Steven and Serfass 2008). Latrine collection sites were chosen to encompass all major
watersheds with consistent river otter activity around Humboldt Bay (Penland and Black
2009). Each site had records of river otter groups ranging in size from 1-9 individuals
and breeding activity with litter sizes ranging from 1-4 pups (Black 2009). Sites
included, from north to south: Little River, Mad River, Arcata Marsh and Wildlife
Sanctuary, Mad River Slough, Woodley Island, Elk River, and the Humboldt Bay
National Wildlife Refuge complex (Figure 1). These seven sites encompassed a
combination of fresh, brackish and salt water river otter activity areas (habitat with
known river otter detections as determined from multiple observations from the citizen
science database) and each had 4-6 river otter latrines (Appendix A).
7
Figure 1. Focal latrine sites sampled non-invasively for river otter scat from 18 May-31
October, 2008, Humboldt Bay, California, USA.
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Non-invasive field collection
Scat and jelly samples were collected from all latrine sites two consecutive
mornings each week from 18 May to 31 October 2008. This sampling period was
established in order to collect enough samples to encompass the entire population and
also ensure recapture of individuals. Only fresh scat and jellies (determined visually by
moistness and odor) were collected to assure samples were deposited within the previous
24 hours. The glossy, mucus sections of scat were targeted in order to reduce prey
material and enhance quality of DNA extractions (Hajkova et al. 2006). In cases of
heavily used or layered latrines, only the top layer of feces was collected to prevent any
contamination between samples. Collected scat and jellies were placed into sterile 50 ml
centrifuge tubes with sterile tongue depressors and stored at -20˚C until DNA extraction
(Arrendal et al. 2007, Lampa et al. 2008). Scat sampling was approved by the Humboldt
State University Animal Care and Use Committee (#07/08.W.40.A) and was consistent
with the American Society of Mammalogist guidelines (Gannon et al. 2007).
Molecular methods
DNA extractions were conducted in an isolation room free of concentrated DNA.
Aerosol-barrier pipette tips were used and work areas and equipment were cleansed with
10% bleach and radiated with ultra-violet (UV) light to reduce contamination. DNA was
extracted from scat and jelly samples using QIAmp® DNA Stool Mini Kit (Qiagen, Inc.,
Valencia, California) following manufacturer’s instructions or with a standard
phenol/chloroform extraction protocol (Ausubel et al. 2003). Negative controls were
executed in all extractions and polymerase chain reactions (PCR) to monitor
9
contamination. PCR set-up was conducted within a UV radiated hood. River otters were
genotyped at six microsatellite loci (Lut453, Lut733, Rio08, Lut701, Rio18, Lut604;
Dallas and Piertney 1998, Beheler et al. 2004, Beheler et al. 2005). Samples were run in
10 microliter (µL) reaction volumes: 2 µL genomic DNA, 10x PCR Gold Buffer, 1 unit
Amplitaq Gold, 1.5 mM MgCl2, 400 µM dNTPs, 0.15 µM reverse and m-13 labeled
primers, 0.30 µM forward primer, 0.7 µM licor m-13 labeled primer, and 0.3 µM bovine
serum albumin (BSA). Thermal cycling temperatures and times followed published
conditions except for Rio08 and Rio18, which were modified to 40 cycles. Products were
read using a LI-COR DNA 4300 Analyzer Gene Readir (LI-COR Biosciences, Lincoln,
Nebraska). GENE PROFILER imaging software was used to estimate allele sizes. To
address potential factors that may have influenced genotyping success, I ran chi-squared
tests to evaluate the impact of diet and sample type (scat, jelly, or mixed scat/jelly) on
PCR success, hereafter referred to as amplification.
Gender was determined using PCR/RLFP (restriction length fragment
polymorphism) analysis of the zink-finger protein gene (ZFX/ZFY; Shaw et al. 2003).
Because this method has not been applied to North American river otters, I validated the
technique using otter tissue samples of known sex (2 males, 2 females). A 447 basepair
(bp) fragment of the ZFX/ZFY gene was amplified with primers P1-5EZ and P2-3EZ
(Aasen and Medrano 1990). PCR product was purified with the QIA-quick PCR
purification kit and sequenced at the San Diego State CSUPERB MicroChemical Core
Facility. Results were analyzed with Code-on-Code software and I identified a unique
restriction enzyme site for TaqαI. This site was identical to the site identified in the
10
Eurasian otter (Lutra lutra), where females yielded one band and males yielded two
(Mucci and Randi 2007, Statham et al. 2007). Since the P1-5EZ/P2-3EZ primer set was
long at 447 bp an internal set of primers which were only 195 bp in length (ZFKF 203L
and AFKF 195H) were tested (Ortega et. al 2004). The latter primer pair amplified more
consistently with degraded fecal DNA obtained in this study. TaqαI digest yielded one
153 bp fragment in females (X band) and two fragments in males (one X band of 153 bp
and one Y band of 203 bp; Figure 2).
PCR was conducted in 10 µL reaction volumes: 2 µL genomic DNA, 10x PCR
Gold Buffer, 1 unit Amplitaq Gold, 1.5 mM MgCl2, 400 µM dNTP, 0.15 forward and
reverse primers, and 0.3 µM BSA. PCR conditions followed Ortega et al. (2004) except
for an increase to 40 cycles. PCR product was digested by restriction enzyme TaqαI at
the following recommended volumes: 10 µL PCR product, 5 units TaqαI, 2 µL Buffer E,
0.2 µL BSA, and 7.3 µL ddH2O (New England Biolabs, Ipswich, MA). The digestion
was incubated at 65°C for 3 hours followed by 80°C for 20 minutes. Products were
visualized on a 2% agarose gel, run at 85 volts for 3 hours, stained with ethidium
bromide, and photographed.
Test of assumptions
A comparative multi-tube PCR approach was followed to create consensus
microsatellite genotypes (Frantz et al. 2003). The comparative multi-tube method
reduces total number of amplifications compared to other approaches (Taberlet et al.
1996), yet still produces reliable genotypes and decreases both cost and time (Hansen et
al. 2007). Genotypes were determined through two positive PCR reactions for
11
Figure 2. Agarose gel showing results of restriction enzyme TaqαI digestion of PCR
product from ZFX/ZFY primer pairs P1-5EZ/P2-3EZ (far left) and ZFKF
203L/AFKF 195H (far right). Primer pair 203L/AFKF 195H yielded one X band
of 153 bp and one Y band of 203 bp; size standard on right in base pair size.
700
12
heterozygotes and three for homozygotes (Frantz et al. 2003, Hansen et al. 2007).
Identical multilocus genotypes were identified using GIMLET (Valière 2002). All scat
samples that produced at least one validated genotype at one microsatellite locus were
sexed using the multi-tube approach. Incomplete multilocus genotypes (less than 5
confirmed loci) were manually grouped based on the most informative loci genotyped,
sex and collection location (Frantz et al. 2003). I used GenAlEx, Genetic Analysis in
Excel (Version 6.3, Peakall and Smouse 2006), to test divergence from Hardy-Weinberg
Equilibrium and to calculate allele frequencies for each locus.
Genotyping errors due to allelic drop-out and false allele rates were calculated
following Broquet and Petit (2004). Allelic drop-out occurred when one allele of a
heterozygous individual did not amplify during a PCR that resulted in a recordable allele.
A false allele was defined as a PCR-generated allele artifact that was a result of
replication slippage. Since allelic drop-out can only occur in heterozygous individuals
(homozygous allelic drop-out simply results in a failed PCR), this error rate is not
representative of all genotypes, but gives a more unbiased estimation of genotyping error
than including homozygous samples in allelic drop-out calculations (Broquet and Petit
2004). Allelic drop-out and false allele weighted averages of all loci were calculated as
an indication of overall data quality.
To evaluate the power of the marker set for individual identification, I calculated
Probability of Identity (PID), the probability that two individuals drawn at random from a
population will have the same genotype at multiple loci (Waits et al. 2001, Valière 2002).
GIMLET was used to calculate PIDunbiased (PID corrected for small sample size) and P(ID)sib
13
(PID among a population of siblings). PID and P(ID)sib provided upper and lower bounds
for identifying the most efficient and error-free set of loci to discriminate individuals.
More loci improve PID but introduce genotyping error especially with poor quality DNA.
Current literature recommends a PID or P(ID)sibs of 0.01 but due to increased genotyping
error higher values are acceptable (Mills et al. 2000, Waits et al. 2001). The extraneous
addition of a false individual is particularly problematic for mark/recapture studies
because it inflates population estimates (Waits and Leberg 2000). Consequently, a
P(ID)sibs between 0.01-0.05 was accepted here.
Abundance estimates
To estimate population size, I ran closed population models using MARK.
Heterogeneity in capture probability among individuals can be difficult to evaluate in
non-invasive sampling and can bias population estimates (Otis et al. 1978, White et al.
1982). To model heterogeneity and time variation in capture probabilities, five models
were built: 1) varying encounter probability by time, 2) allowing encounter probability
and recapture probabilities to differ (behavioral response), 3) holding encounter
probability constant, 4) including heterogeneity among individuals and holding encounter
probability constant, and 5) including heterogeneity among individuals and varying
encounter probability by time. Sexes were combined in all models because sample sizes
were too small to include sex as a group variable. Models were ranked using corrected
Akaike Information Criterion (AICc; Lukacs and Burnham 2005). Closed capture-
recapture model notation in the literature sometimes still follows Otis et al. (1978), but
6
0
0
14
with the extensive capabilities available in MARK, I chose to use extended model
notation to describe the closed-population models (Lukas and Burnham 2005).
Capture histories for individual river otters were constructed using repeated
detections of the same genotype. Sampling occasions were broken into week sessions; if
multiple captures of an individual occurred during two consecutive days, they were
pooled and recorded as a single detection for the week. To ensure population closure and
to evaluate different sampling period durations, two separate capture histories were built.
For the first modeling regime I used detections from August and September; for the
second I used detections only from September. Both periods were after early summer
months of high pup mortality and before fall dispersal events (Melquist and Hornocker
1983). River otters only occur near water bodies with sufficient food resources, thus to
meet the assumption that every individual had a reasonable probability of detection, I
attempted to survey all major watersheds around Humboldt Bay. Although there was a
possibility that additional river otters occurred in small, un-surveyed areas, I focused on
areas with consistent river otter activity over the past 10 years (Black 2009). I
determined overall detection probability by taking each sessions estimated capture
probability (p), subtracting it from 1, then multiplying those values together and finally
subtracting that overall product from one (1-[(1-p1)(1-p2)...(1-pi)]); personal
communication, T. L. George 2010. HSU, 1 Harpst Street, Arcata, CA 95521). This
value provided an estimated probability of capture.
15
Genetic population structure and relatedness
To determine the number of genetically divergent groups (K) in the Humboldt
Bay river otter population I performed a cluster analysis and a Discriminant Analysis of
Principal Components using ADEGENET 1.2-3 for Program R (Jombart 2008, Jombart
et al. submitted). The cluster function was designed to infer K from multi-locus genetic
data. The analysis transforms the data using a principal component analysis and then
runs successive models with an increasing number of clusters. For each model, a
statistical measure of goodness of fit (by default, Bayesian Information Criterion) was
computed, and the number of clusters in the data was identified when subsequent K
values no longer led to an appreciable improvement in fit. I graphically investigated
population differentiation using the Discriminant Analysis of Principal Components. The
Discriminant Analysis of Principal Components aims to maximize between-group
variability and achieve the best discrimination of genotypes into predefined clusters.
Sampling site locations were used as prior population information. If an individual
genotype was detected at multiple sampling locations, the site with the most visits was
used to define the individual’s prior population assignment. This multivariate approach
has been shown to reliably cluster individuals based upon genotypes and does not make
assumptions regarding Hardy–Weinberg or linkage equilibrium (Jombart et al. 2008).
An assignment test using the frequency-based Paetkau et al. (1995) criterion was
run in GeneClass2 (Version 2.0, Piry et al. 2004). Assignment tests provide an indication
of population structuring based on whether samples can be reliably assigned to their
location of origin. To compute the probability individuals belonged to each sampling
16
location, 10,000 Monte-Carlo simulations were run with a leave-one-out resampling
algorithm. Samples not assigned correctly were examined to see if sex or location was an
indication of poor assignment using a Fisher’s exact test.
Pairwise relatedness (R) values (Queller and Goodnight’s 1989) were calculated
with GenAlEx. Relatedness coefficients range from -1 to 1, negative values denote no
relation and positive values denote relatedness. R coefficients of approximately 0.25 and
0.50 suggest half-sibling and full-sibling levels of relatedness, respectively (Konovalov
and Heg 2008). R values were evaluated to see if mean relatedness within sampling
locations was greater than mean relatedness of all other pair-wise comparisons. I
repeated the same analysis without genotypes of individuals detected at multiple sites to
see if roaming individuals were not part of potential resident family groups utilizing a
site. I also calculated relatedness between and among sexes. A total of 999 permutations
were completed to create mean R values, 95% confidence intervals, and to evaluate
significance of R among and between sampling locations.
A Mantel test (Isolation by Distance Web Service version 3.61; Jensen et al.
2005) was used to evaluate a genetic isolation by distance model for all river otters in the
study area as a whole and after removing the Little River site. Little River was the
furthest northern site, and not technically within Humboldt Bay proper, thus removing the
site allowed evaluation of isolation by distance within the Bay. Mantel’s test is
specifically designed for pair-wise values such as genetic and geographic distances that
are not independent of each other. I used R values as the measure of genetic distance.
Methods similar to Dallas et al. (1999) and Blundell et al. (2002b) were used to calculate
17
geographic ‘otter distance’ defined as the route most likely taken by a river otter via
waterways rather than determining the straight line distance over land. This ‘otter
distance’ was measured as the most direct route between midpoints of each sampling
location following a linear course parallel to the shoreline and the shortest distance
possible overland when necessary.
18
RESULTS
Field sampling and molecular methods
From 18 May to 31 October 2008, I collected 357 river otter scats, 82 jellies and
44 mixed scat/jelly samples (Table 1). Among the 483 collections 124 (25.7%) were
successfully genotyped (Table 2). The largest number of collections (n=110) and new
genotypes detected (first time captures; n=17) were in June, while the most samples
genotyped (first time captures and recaptures) was in September (n=24, Figure 3). There
was a significant difference in sampling location and number of genotypes detected at
each location (χ2
6=16.40, P=0.01). When Elk River and Humboldt Bay National Wildlife
Refuge were removed from the analysis, there was not a significant difference among
sites (χ2
5=8.87, P=0.11 and χ
2
5=5.75, P=0.33, respectively). Thus either Elk River otters
had more samples successfully genotyped, or Humboldt Bay National Wildlife Refuge
had fewer samples successfully genotyped. Prey type found in scat did not influence
amplification success of scat (χ2
3=1.15, P=0.76, Table 3). Jellies amplified significantly
better than scat (χ2
2=43.86, P<0.001, Table 4). When scat samples were removed from
the analysis, there was a marginal but non-significant difference in amplification success
between jelly and mixed scat/jelly samples (χ2
2=4.94, P=0.08).
Test of assumptions
All six microsatellite loci were polymorphic, the number of alleles ranged from
2-4 (Table 5). A global test calculated over all sampling locations showed all loci met
Hardy-Weinberg equilibrium assumptions; observed heterozygosity (HO) ranged 0.361-
18
19
Table 1. Total number of river otter scat, jelly, and mixed scat with jelly samples
collected throughout the Humboldt Bay region, California, USA from 18 May-
31 October, 2008. Samples from north to south were Little River, Mad River,
Arcata Marsh and Wildlife Sanctuary (AMWS), Mad River Slough (MRS),
Woodley Island, Elk River, and Humboldt Bay National Wildlife Refuge
complex (HBNWR).
Little Mad AMWS MRS Woodley Elk HBNWR Total
Scat 23 40 68 36 14 103 73 357
Jelly 8 8 17 10 3 25 11 82
Scat and jelly 4 0 4 2 6 18 10 44
Total 35 48 89 48 23 146 94 483
20
Table 2. Genotyping success of river otter DNA extracted from scat samples in the
Humboldt Bay region, California, USA from 18 May-31 October, 2008.
Sampling sites from north to south were Little River, Mad River, Arcata
Marsh and Wildlife Sanctuary (AMWS), Mad River Slough (MRS),
Woodley Island, Elk River, and Humboldt Bay National Wildlife Refuge
complex (HBNWR).
Little Mad AMWS MRS Woodley Elk HBNWR Total
Genotyped 9 13 24 8 7 51 12 124
Failed 26 35 65 40 16 95 82 359
Total Collected 35 48 89 48 23 146 94 483
% Success 25.7 27.1 27.0 16.7 30.4 34.9 12.8 25.7
21
Figure 3. Total number of river otter scat samples collected, total number of
individuals successfully genotyped, and newly detected genotypes in the
Humboldt Bay region, California, USA, from 18 May-31 October, 2008.
*May only sampled for 2 weeks.
0
20
40
60
80
100
120
May June July August September October
Nu
mb
er
Scat collected
Individuals genotyped
New genotype
*
22
Table 3. Diet and amplification success of river otter DNA extracted from
scat samples collected in the Humboldt Bay region, California,
USA from 18 May-31 October, 2008.
Scat Prey Content
Fish Crab Invert Bird Mixed Unknown
Genotyped 47 6 0 2 9 1
Failed 183 38 6 9 34 22
Total 230 44 6 11 43 23
% success 20.4 13.0 0 18.0 20.9 4.3
23
Table 4. Sample type, [scat, jelly or mixed samples (scat and
jelly)], and genotyping success of river otter DNA
extracted from samples collected in the Humboldt Bay
region, California, USA from 18 May-31 October, 2008.
Scat Jelly Scat and Jelly
Genotyped 65 43 16
Failed 292 39 28
Total 357 82 44
% success 18.0 52.4 36.0
24
Table 5. The number of alleles, observed heterozygosity (HO), expected heterozygosity
(HE), tests for conformance to Hardy-Weinberg equilibrium, and allele sizes and
frequencies of six microsatellite loci, among 41 river otters in the Humboldt Bay
region, California, USA, from 18 May-31 October, 2008.
Locus No.
Alleles HO HE P Allele size(bp)/frequency
Lut453 4 0.692 0.651 0.675 142/0.18 144/0.04 146/0.32 150/0.46
Lut733 3 0.650 0.587 0.683 171/0.23 174/0.56 179/0.20 Rio08 3 0.485 0.606 0.150 217/0.53 219/0.20 223/0.27 Lut701 4 0.541 0.537 0.480 194/0.64 198/0.11 202/0.22 206/0.04
Rio18 3 0.487 0.444 0.565 156/0.72 162/0.15 172/0.13 Lut604 2 0.361 0.453 0.222 133/0.35 139/0.65
Mean 3.27 0.536 0.546 0.462
25
0.692 and expected heterozygosity (HE) ranged 0.444-0.651 (Table 5). When broken into
sampling locations, the only location not in Hardy-Weinberg equilibrium was Elk River
at locus Rio08 (P=0.01). Allelic dropout rates ranged from 22.6-32.5%, with a weighted
average over all six loci of 28.6% (Table 6). False alleles occurred in 22 of 2,200 PCR
reactions and ranged from 0.6-1.5% among loci (Table 6). When evaluated for all six
microsatellites, PID and P(ID)sib were 0.00037 and 0.026, respectively (Figure 4).
Abundance estimates
Among the 124 scat samples genotyped, 40 unique microsatellite genotypes were
identified (Appendix B). However, two individuals were identical at all six loci but
different in sex typing, providing a conservative minimum count of 41 river otters. Of
these 41 individuals, 22 were males, 16 females, and 3 were unknown. The number of
recaptures ranged from 0 to 12 (mean=3.0 ± 0.39 (SE)). There was no significant
difference in number of recaptures between males (mean=3.3 ± 0.65) and females
(mean=2.7 ± 0.38; t37=0.83, P=0.42). Only eight individuals were detected at more than
one sampling location, of which seven were males and one was female (Table 7,
Appendix C). There were five instances of pairs of individuals detected at the same site
on the same date (Table 8). The largest distance moved was by a male river otter (Ott20).
Ott20 was detected moving from Woodley Island (7 July) to Elk River (19 Aug) to
Arcata Marsh and Wildlife Sanctuary (25 Aug) back to Woodley Island (23 Sept) then
Elk River again (28 Sept) then Humboldt Bay National Wildlife Refuge (29 Sept) and
back to Elk River (5-26 Oct), a total distance of approximately 60 km over 4 months
(Table 7, Figure 5). This was a linear path from north to south of approximately 26 km.
26
Table 6. Repeat motifs, Probability of Identity (PID), sibling Probability of Identity
(P(ID)sibs), allelic dropout rates (ADO), and false allele rates (FA) at six loci,
among 41 river otters in the Humboldt Bay region, California, USA, from 18
May-31 October, 2008.
Locus Repeat motif PID P(ID)sibs ADO rate% FA%
Lut453 (CA)26 0.19 0.47 30.9 1.0
Lut733 (GATA)4GAT(GATA)12 0.24 0.52 28.2 1.2
Rio08 (TG)15 0.22 0.50 32.5 1.5
Lut701 (GATA)11GAA(GATA)2GAA(GATA)4 0.26 0.55 29.8 1.1
Rio18 (CT)6(CTAT)14 0.35 0.62 22.6 0.6
Lut604 (CA)26 0.40 0.62 24.1 0.7
Over all loci 0.0004* 0.026* 28.6** 1.0**
*Multiplied together to get over all loci values
**Weighted average
27
Figure 4. Probabilities of identity (PID) from river otter genotypes sampled
non-invasively from the Humboldt Bay region, California, USA,
from 18 May-31 October 2008. Probabilities were calculated for six
microsatellite loci and arranged in order of increasing PID value.
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Lut453 Lut733 Rio08 Lut701 Rio18 Lut604
Pro
ba
bil
ity
of
Iden
tity
Loci
PID
PIDsibs
28
Table 7. Home site (location most often detected), sex, sites detected at, and total linear
distance traveled (km; sometimes on multiple trips) for all river otters detected at
multiple sites in the Humboldt Bay region, California, USA from 18 May-31
October, 2008.
Home site* Animal code Sex Sites visited Total distance
traveled (km)
AMWS Ott16 M MRS, AMWS 4
AMWS Ott27 M AMWS, Elk 17
AMWS Ott33 M AMWS, MRS, Mad 11
MRS Ott18 F HBNWR, MRS 21
Elk Ott1 M Elk, HBNWR 26
Elk Ott8 M Woodley, Elk 21
Elk Ott20 M Woodley, Elk, AMWS, HBNWR 60
Elk Ott40 M Elk, HBNWR 9 *Sites from north to south were Mad River (Mad), Arcata Marsh and Wildlife Sanctuary (AMWS), Mad River Slough
(MRS), Woodley Island (Woodley), Elk River (Elk), and Humboldt Bay National Wildlife Refuge (HBNWR).
29
Table 8. Pairs of roaming river otters detected at the same site on the
same date and pairwise relatedness values (Queller and
Goodnight’s R values from GenAlEx) between dyads in the
Humboldt Bay region, California, USA from 18 May-31
October, 2008.
Pairs Site* Date R value
Ott8/Ott20 Woodley 7/7 -0.5777
Ott8/Ott20 Elk 8/19 -0.5777
Ott8/Ott20 Elk 10/13 -0.5777
Ott20/Ott27 AMWS 8/25 0.8492
Ott8/Ott40 Elk 9/8 -0.4465
* Sites were Arcata Marsh and Wildlife Sanctuary (AMWS), Woodley Island (Woodley), and
Elk River (Elk).
30
Figure 5. Movement patterns for the only 8 river otters detected at multiple sites in
the Humboldt Bay region, California, USA, from 18 May-31 October,
2008. Circles denote sampling site and the lines and arrows indicate paths
of movement.
31
Roaming river otters increased the number of individuals detected at sites. Each
sampling site had multiple individual detections, but individuals sampled at Woodley
Island were all more often detected at Elk River, and were therefore grouped with Elk
River animals (Table 9). Overall density, using minimum genotypes detected (41) over
45 km of linear coast line, was 0.9 river otters/ km.
Mark/recapture models designed to estimate population size were compared to
the total number of genotypes detected and used to evaluate the proportion of the
population sampled. The first model set based on samples collected during August and
September yielded 29 unique genotypes and the second model set based on September
alone had 24 unique genotypes. Both modeling efforts yielded the same top closed
population model which held encounter probability (p) and recapture probabilities (c)
equal but varied by time (Table 10). Encounter probability estimates and associated
standard errors were reasonably small and varied widely between capture occasions
(Appendix D). Overall capture probabilities were 85.6% and 73.0%, respectively, for the
2-month and single-month capture histories. The 95% confidence intervals around
abundance estimates ranged 30-44 individuals and 26-50 individuals for the 2-month and
single-month capture histories, respectively. Both modeling regimes encompassed the
minimum number of genotypes (41) detected (Table 11). Given these ranges and the
high overall capture probabilities, the majority of Humboldt Bay river otters were
sampled.
32
Table 9. Total number of male, female and unknown sex river otters (and
maximum number including roaming otter visitations) detected at each
site using non-invasive genetic samples from the Humboldt Bay region,
California, USA, from 18 May-31 October 2008.
Little Mad AMWS MRS Elk HBNWR Total
Males 2 4 (5) 3 (4) 0 (2) 11 (12) 2 (5) 22
Females 1 3 5 3 3 1 (2) 16
Unknown 1 1 1 0 0 0 3
Total 4 8 (9) 9 (10) 3 (5) 14 (15) 3 (7) 41 *Sites from north to south were Little River (Little) Mad River (Mad), Arcata Marsh and Wildlife
Sanctuary (AMWS), Mad River Slough (MRS), Elk River (Elk), and Humboldt Bay National Wildlife
Refuge (HBNWR).
33
Table 10. Closed population model rankings from program MARK for river otters
captured in August-September 2008 and only September 2008, in the Humboldt
Bay region, California, USA. Corrected Akaike Information Criterion (AICc) was
used to rank models.
*N=abundance estimate parameter, p=capture probability, c=recapture probability, (t) denotes temporal
variation, (.) denotes being held constant, π denotes heterogeneity model, a and b are heterogeneity periods.
**K=number of parameters.
Model* Description Δ AICc AICc
Weights K** Δ AICc
AICc
Weights K**
{(N,p(t)=c(t))} Time varying p 0.00 0.85 10 0.00 0.75 6
{(N,p(.),c(.))} Behavioral response 4.37 0.10 3 3.17 0.15 2
{(N,p(.)=c(.))} Constant p 6.36 0.04 2 5.26 0.05 3
{(N,π,pa(.)=ca(.),pb(.)=cb(.))} Heterogeneous p 8.40 0.01 3 6.73 0.03 4
{(N,π,pa(t)=ca(t),pb(t)=cb(t))} Heterogeneous time varying p 10.85 0.00 20 8.71 0.01 12
August - September September
34
Table 11. Population estimates and corresponding standard error (SE) and 95%
confidence intervals (CI), from top ranked closed population models for river
otters sampled from August-September, 2008, in the Humboldt Bay region,
California, USA. Estimates were evaluated for August-September combined
capture histories and September alone.
Model* N SE Lower 95% CI Upper 95% CI
{(N,p(t)=c(t))} Aug-Sept 33.38 3.06 30.27 44.07
{(N,p(t)=c(t))} Sept 32.36 5.44 26.61 50.77
*N=abundance estimate parameter, p=capture probability, c=recapture probability, (t) denotes temporal
variation.
35
Genetic population structure and relatedness
Based on the cluster analysis, there were 6-9 distinct genetic clusters (Figure 6).
Bayesian Information Criterion (BIC) value leveled off between 6-7 clusters (BIC=15.96
and 15.91 respectively), was lowest at 8 genetic clusters (BIC=15.50) and began to
increase again at 9 (BIC=15.96). This indicated that 6-9 clusters was the optimum
number of genetically differentiated groups. The Discriminant Analysis of Principal
Components output visually expressed the differentiated groups (Figure 7). The central
sampling locations (Mad River, Arcata Marsh and Wildlife Sanctuary, Mad River
Slough, and Elk River), overlapped in allelic contribution, while Little River to the north
and Humboldt Bay National Wildlife Refuge complex to the south were the most
genetically divergent from the main group. Despite overlap among central locations, they
were still grouped as separate clusters. Most of the variance was explained by the first
and second principle components as shown by Eigenvalues (Figure 7). Overall
assignment probability for the Discriminant Analysis of Principal Components, that is,
the percent of individuals correctly grouped to their true sampling location during the
initial steps of the Discriminant Analysis of Principal Components analysis to minimize
variance, was 85.7%.
The assignment test correctly assigned individuals to their location of origin 73%
(30/41) of cases (Table 12). The number of incorrectly assigned samples was
significantly different among sampling sites (Fisher’s Exact test, P=0.001), with most
incorrectly assigned individuals originating from Elk River. Five out of six individuals
incorrectly assigned from Elk River were males, but overall there was no difference in
36
Figure 6. Bayesian Information Criterion (BIC) values for models with increasing
clusters to determine the number of genetically differentiated groups
derived from multi-locus genetic data of 41 river otters in the Humboldt
Bay region, California, USA, from 18 May-31 October 2008.
37
Figure 7. Discriminate Analysis of Principal Components constructed from 41 river otter
genotypes determining genetic differentiations between locations in the Humboldt
Bay region, California, USA, from 18 May-31 October 2008. The locations from
north to south were Little River, Mad River, Arcata Marsh Wildlife Sanctuary
(Marsh), Mad River Slough (MRS), Elk River, and Humboldt Bay National
Wildlife Refuge complex (HBNWR). Eigenvalues (insert) show the majority of
variance was captured within the first and second principal component (PC).
38
Table 12. The number of correct (assigned same location as sampled) and incorrect
(assigned different location than sampled) assignments based on log-
likelihood values for 41 river otter genotypes in the Humboldt Bay region,
California, USA, from 18 May-31 October, 2008.
Location* Correctly
assigned Incorrectly
assigned Location incorrectly assigned (# samples)
Little 4 0 Mad 7 1 MRS (1)
AMWS 7 2 MRS (2)
MRS 1 2 AMWS (1), Elk (1)
Elk 8 5 Little (1), Mad (1), MRS (2), AMWS (1)
HBNWR 3 0 *Sites from north to south were Little River (Little) Mad River (Mad), Arcata Marsh and Wildlife
Sanctuary (AMWS), Mad River Slough (MRS), Elk River (Elk), and Humboldt Bay National Wildlife
Refuge (HBNWR).
39
sex of incorrectly and correctly assigned individuals (Fisher’s Exact test, P=0.45).
River otters were more related to individuals within sampling locations compared
with individuals among all sampling sites (Tables 13, 14). Differences were significant
for all sites except for Mad River Slough and Elk River (Table 14). When roaming
individuals, i.e., genotypes detected at more than one site, were removed from
the analysis, there was little change in R-values, except for the Elk River site (Figure 8).
At Elk River, within site relatedness became significantly greater compared with
individuals among all sites (P=0.043) and the mean R value, although still low, increased
(from 0.03 to 0.10). After removing roaming individuals, Mad River Slough only had
two genotypes left making bootstrapping impossible, thus it was removed from the
analysis. Relatedness among males and among females (analyzed separately) was low
and there was no difference within sex compared to between sex R values (Figure 9). I
was unable to compare R values between males and females within sites due to small
sample sizes. Isolation by distance regression with R values was not significant (Mantel
Test, correlation statistic=0.02, P=0.439, R2=0.35%, Figure 10a). When comparisons
involving Little River were removed from the analysis, there was a significant positive
correlation between geographic and genetic distance (Mantel test, correlation
statistic=-0.64, P=0.035, R2=41.10%; Figure 10b).
40
Table 13. Relatedness coefficients (R) between study locations (within locations on
diagonal) in the Humboldt Bay region, California, USA, calculated from
river otters non-invasively sampled 18 May-31 October 2008.
Sampling location* Little Mad AMWS MRS Elk HBNWR
Little 0.2944 -- -- -- -- --
Mad 0.0478 0.3088 -- -- -- --
AMWS -0.2942 -0.0552 0.1563 -- -- --
MRS -0.3153 0.0728 0.1905 0.0698 -- --
Elk -0.0943 0.0128 -0.0949 -0.0783 0.0319 --
HBNWR 0.2626 -0.0774 -0.2314 -0.2988 -0.1332 0.5860 *Sites from north to south were Little River (Little) Mad River (Mad), Arcata Marsh and Wildlife
Sanctuary (AMWS), Mad River Slough (MRS), Elk River (Elk), and Humboldt Bay National Wildlife
Refuge (HBNWR).
41
Table 14. Mean coefficient of relatedness (Queller and Goodnight’s
R-values from GenAlEx) within and between study areas for
river otter genotypes detected in the Humboldt Bay region,
California, USA, from 18 May-31 October 2008.
Location* Mean within Mean between P
Little 0.294 -0.079 0.044
Mad 0.310 0.000 0.001
AMWS 0.156 -0.097 0.022
MRS 0.070 -0.086 0.330
Elk 0.032 -0.078 0.136
HBNWR 0.586 -0.096 0.009
Mean 0.241 -0.072 *Sites from north to south were Little River (Little) Mad River (Mad), Arcata Marsh and Wildlife
Sanctuary (AMWS), Mad River Slough (MRS), Elk River (Elk), and Humboldt Bay National Wildlife
Refuge (HBNWR).
42
a)
b)
Figure 8. Mean relatedness values (Queller and Goodnight’s R values from
GenAlEx) within sampling locations for (a) all river otters sampled and
(b) for only river otters that exhibited no movement between sites, in the
Humboldt Bay region, California, USA, from 18 May-31 October 2008.
Sites from north to south were Little River (Little) Mad River (Mad),
Arcata Marsh and Wildlife Sanctuary (AMWS), Mad River Slough
(MRS), Elk River (Elk), and Humboldt Bay National Wildlife Refuge
(HBNWR). Upper and lower whiskers denote 95% confidence limits
determined by 999 bootstrap resampling. Female (♀), male (♂), and
unknown sex (?) sample sizes displayed above sampling sites.
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
Little Mad AMWS MRS Elk HBNWR
R
Sampling location
♀=1
♂=2
?=1
♀=5
♂=3
?=1
♀=3
♂=4
?=1
♀=3
♂=0
?=0
♀=3
♂=11
?=0
♀=1
♂=2
?=0
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
Little Mad AMWS Elk HBNWR
R
Sampling location
♀=1
♂=2
?=1
♀=3
♂=4
?=1
♀=5
♂=0
?=1
♀=3
♂=7
?=0
♀=1
♂=2
?=0
43
Figure 9. Mean relatedness values (Queller and Goodnight’s R values from
GenAlEx) for male and female river otters sampled in the Humboldt Bay
region, California, USA, from 18 May-31 October 2008. Upper and lower
whiskers denote 95% confidence limits determined by 999 bootstrap
resampling.
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Male Female
R
Gender
n=16n=22
44
a) All sampling locations
b) Little River removed
Figure 10. A test of isolation by distance for river otters detected at all sampling
locations (a) and all locations but Little River (b) in the Humboldt Bay
region, California, USA from 18 May-31 October 2008. Genetic distance
was measured by R values and geographic distance was measured in linear
distance (km).
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0 10 20 30 40 50
Gen
etic
dis
tan
ce (
R)
Geographic distance (km)
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0 10 20 30 40 50
Gen
etic
dis
tan
ce (
R)
Geographic distance (km)
45
DISCUSSION
Non-invasive genetic surveys are an effective way to monitor elusive carnivores
(Banks et al. 2003, Ruell et al. 2009, Beja-Pereira et al. 2009). Even with relatively low
genotyping success, by employing an extensive sampling regime I was able to census
almost all Humboldt Bay area river otters. This conclusion was supported by the two
modeling efforts as the confidence intervals encompassed the total number of unique
genotypes and by the lack of new genotype detections in October suggesting most
individuals were sampled before the end of the study (Table 11, Figure 3). The ability to
capture a majority of individuals non-invasively was possible because of the ease of
sampling latrines, which serve as a communicative tool integral in social interactions of
river otters and are therefore frequently used (Hornocker et al. 1983, Kruuk 1992,
Melquist et al. 2003, Rostain et al. 2004, Oldham and Black 2009). Based on these
results, non-invasive sampling during summer months can provide sufficient data for
population estimates applicable for monitoring coastal California river otters, as well as
other similar populations.
The 25.7% genotyping success for all samples herein was similar to published
river otter fecal DNA studies (20% Dallas et al. 2003, 24% Kalz et al. 2006, 40% Prigioni
et al. 2006), and as expected, was lower than other non-invasive genetic studies
(Palomares et al. 2002, Oretga et al. 2004, Solberg et al. 2006). Overall genotyping error
rates (average allelic drop out=28.63%, average false allele=1.00%) were also
comparable to other non-invasive genetic studies that defined and calculated unbiased
allelic drop out and false allele rates (Broquet and Petit 2004). Thus, my genotyping
45
46
error rates were within current literature trends acceptable for individual identification.
With slightly higher P(ID)sibs, there was not differentiation between at least two closely
related individuals, but this was considered a less costly mistake than adding ghost
individuals due to genotyping error (Mills et al. 2000, Waits and Leberg 2000). In
mark/recapture studies built from genotypes, a shadow effect (when two individuals are
lumped as one) may result in a lowered N but this bias tends to be small, unlike
extraneous ghost genotypes which quickly inflate N (Mills et al. 2000, Waits and Leberg
2000). A conservative population estimate was more desirable for the purpose of
evaluating Humboldt Bay river otters than grossly overestimating N with ghost
individuals.
Unequal capture probability is another potential cause of underestimating
abundance and is a long-standing issue for population studies (Otis et al. 1978). To
account for unequal detection probabilities, I modeled five different scenarios of capture
variation. Both capture history regimes’ top models included temporal variation of
capture probability between sampling sessions while holding equal capture and recapture
probabilities. For the August-September capture history, the time varying model had
overwhelming support with 85% of AICc weight. With relatively narrow confidence
intervals encompassing the minimum number of genotypes detected (Table 11), this
model performed well estimating abundance. The capture history from September alone
produced very similar results, receiving 75% of AICc weight. Confidence intervals were
larger, but overall this model also performed well with only one month of data (Table
11). Heterogeneity models (models that correct for initial capture probabilities differing
47
among individuals of a population) have been shown to limit error in other non-invasive
studies (Mills et al. 2000, Solberg et al. 2006, Ruell et al. 2009), but were the lowest
ranking models in my analyses. This was due to strong temporal differences in capture
probabilities, shadowing any other individual heterogeneity in capture histories.
The large temporal variation in capture probability could have been caused by two
factors: either there was a difference in defecation rate among sampling occasions or a
difference in genotyping success among sampling occasions. I suspect both of these
factors played a role. Changes in latrine usage would indicate behavioral variation in
scent marking by individuals over time. Several studies documented male and females
river otters scent mark at the same rate (Dallas et al. 2003, Janssens et al. 2008).
Similarly, I detected no difference between sexes in detection rates. Variation in
genotyping success probably contributed to temporal differences in capture occasion
considerably more than changes in defecation rates since genotyping success rates were
low. Temporal variation could be due to a number of uncontrollable environmental
factors. Non-invasive fecal studies have found that diet, time of collection, and weather
conditions can affect amplification success and therefore encounter rates (Murphy et al.
2003, Nsubuga et al. 2004, Hajkova et al. 2006). These factors may have been negligible
here since diet did not influence amplification rates. Samples were only collected in
morning hours and the Pacific Northwest is a temperate region with comparatively little
temporal variation in temperature. Laboratory methods could also account for some
genotyping variation, but every measure was taken to treat samples identically and I was
the only individual processing samples, which maintained consistency. Despite trying to
48
control these factors, a relatively extended mark/recapture sampling regime for a closed
population model (i.e., longer than a few days or weeks), was necessary to capture the
variation and accurately model detection probability, thereby providing unbiased N
estimates.
Determining the length of the sampling period is critical for meaningful closed
population modeling. It is important to create a sampling regime long enough to capture
sufficient numbers of individuals in a population without violating geographic and
demographic closure assumptions (White et al. 1982). Due to low genotyping success
with river otters it can be difficult to achieve both of these at the same time. Given
reasonable model estimates, strong support for top models, and considering both models
had almost identical results, it appears population closure was maintained and an
adequate number of individuals were sampled. Since the models performed so similarly,
but the single month had wider confidence limits, I would suggest a 2-month sampling
regime if time and money allow. Future non-invasive river otter studies focused on
abundance estimates could sample intensively August and September, encompassing
temporal changes in capture probabilities and producing river otter population estimates
comparable to these results.
Based on minimum genotypes detected and the top confidence interval range
from mark/recapture model estimates, there were between 41-51 river otters throughout
the Humboldt Bay region. Initially, this appeared to be a large number of river otters in
one bay as compared to other studies (Testa et al. 1994, Bowyer et al. 2003). Among
several bays in Alaska, the highest density range was 64 river otters per 138.6 km of
49
linear coast line (0.46 river otter/km; Bowyer et al. 2003). In Humboldt Bay, I found a
minimum of 41 river otters per 45 km of linear coast line (0.93 river otter/km). However,
the inability to discriminate juveniles from adults inflated overall resident population
estimates since some juveniles will disperse out of the area and would not become
resident breeding adults. Considering this, the density of river otters in the Humboldt
Bay area was likely similar to other coastal systems.
Evaluation of genetic structure was representative of a majority of Humboldt Bay
river otters because a high proportion of the population was detected. Sample sizes were
small but this was not due to a lack of data or poor sampling design. Rather, it was
simply the number of animals in the study area. By utilizing several methods to assess
genetic differentiation I could be confident in congruent results even with small sample
sizes. Overall there was faint but detectable population structuring of river otters
sampled at different geographic locations indicating the presence of social groups. The
cluster analysis found 6-9 distinct groups, approximately matching the number of
sampling locations. This was not a result of a priori population assignment because the
cluster function does not incorporate location data. The assignment test also supported
genetically differentiated groups with 73% of individuals correctly assigned to their
location of origin. The Discriminate Analysis of Principal Components (Figure 7)
showed some genetic overlap among the central locations. Most notably Mad Rive
Slough overlapped with Mad River, Arcata March and Wildlife Sanctuary, and Elk River.
Given the few individuals detected at Mad River Slough (n=3), large genetic overlap with
neighboring sites, and low R values, it was not a distinct group. Remaining sites
50
displayed visible clustering, that based on relatedness values were likely related family
groups.
Mad River, Arcata March and Wildlife Sanctuary, and Elk River had between 8-
14 unique individuals (Table 8), a higher number than one solitary female and her pups
(3-5; Melquist and Hornocker 1983). Give this, it is likely more than one family group
used the same areas. Female-based family groups have been observed where mature
female river otters will remain with or join a group composed of her mother and pups,
even at times helping rear the young (Shannon 1989, Rock et al. 1994). R values at Mad
River (R=0.3) and AMWS (R=0.2) were close to half-sibling values (R=0.25), suggesting
social family groups were utilizing the same space and tolerant of spatial overlap with
other potentially unrelated mature individuals. Little River and Humboldt Bay National
Wildlife Refuge had fewer individuals detected, 4 and 3 respectively. Little River R
values (R=0.3) were also close to half-sibling values and Humboldt Bay National
Wildlife Refuge R values (R=0.6) were as high as full-sibling values (R=0.50). Based on
the few genotypes detected and the high relatedness coefficients, these two sites most
likely consisted of a single female and her pups. Solitary females have been found to be
prevalent at the same time as more social river otters (Melquist and Hornocker 1983,
Blundell et al. 2002a), so it is possible to have different social strategies observed within
one region. I was unable to detect differences in relatedness between males and females
due to small sample size and because of an inability to distinguish juveniles from adults.
As a result, any potential difference between the sexes was masked by relatedness
between parent-offspring dyads.
51
Melquist and Hornocker (1983) reported variation in daily movements of river
otters, with the mean daily distance traveled being approximately 5 km or less. The
largest daily movement detected in this study was individual Ott20, a male, traveling
about 8.5 km over a 24-hr period as evidence by being detected at Elk River on 28 Sept
and then Humboldt Bay National Wildlife Refuge on 29 Sept. Although there were other
individuals detected at multiple sites (Table 7, Figure 5), Ott20 moved the most. All but
one of the roaming river otters were males. Among otters, the formation of bachelor
groups (large gregarious groups of unrelated males and occasionally non-reproductive
females) is unique to North American river otters (Shannon 1989, Kruuk 1995, Blundell
et al. 2002a, Hansen et al. 2009). Roaming river otters could be an indication of bachelor
group formation, as there were five instances of two roaming males being detected at the
same site on the same date. This suggests some degree of gregarious behavior and space
sharing. Relatedness values varied between these pairs and several were found together
repeatedly over a 4-month period (Table 8). Also, when roaming individuals were
removed from relatedness analyses, the mean Elk River R value became greater,
suggesting that roaming animals were not part of the Elk River family group. The spatio-
temporal distribution of scat for a few roaming individuals supports the existence of at
least short-term bachelor groups alongside larger related family groups.
All sites, except for Little River and Humboldt Bay National Wildlife Refuge,
exhibited significant isolation by distance gene flow. The isolation by distance analysis
showed relatedness values decreased as geographic distance between sites became larger.
The likely presence of family groups could create significant isolation by distance gene
52
flow. Detected trends also explain river otter dispersal patterns, indicating that low rates
of natal dispersal within Humboldt Bay may cause gene flow to be spatially restricted, as
documented in other river otter populations (Melquist and Hornocker 1983, Blundell et
al. 2002b). The presence of limited dispersal and gene flow has important conservation
and management implications applicable to river otter populations elsewhere. Low levels
of dispersal may decrease or slow natural recolonization if local populations decline or
are extirpated. Limited gene flow increases genetic differentiation within populations so
management actions involving river otter translocation should be undertaken cautiously
to avoid loss of local genetic diversity (Blundell et al. 2002b).
Little River and Humboldt Bay National Wildlife Refuge complex were the most
geographically distant sites and yet were more genetically similar to each other than to
central groups. This is contradictory to the isolation by distance gene flow to which the
rest of the sites conformed. One potential explanation for this could be rare dispersal
events of genetically similar females that led to established home ranges at
geographically distant sites. River otters display low levels of male-biased natal dispersal
(Kruuk 1995, Blundell et al. 2002b, Dallas et al. 2003, Janssens et al. 2008). Females are
mostly philopatric, but when dispersal events do occur, females have been reported to
disperse larger distances than males, traveling up to 60-90 km (Blundell et al. 2002b),
more than the distance between Little River and Humboldt Bay National Wildlife Refuge
(~45 km). These two locations currently appear to each host one distinct family group,
but it is possible that the female founding members of these small groups were related
and separated due to a female dispersal event.
53
In general, the Elk River otters stood out among those at all other sites. Elk River
had the highest number of animals detected, these individuals were less related to one
another than river otters at other sites were to one another, and there was a trend of
greater movement among individuals detected most often at Elk River. Five of the eight
roaming river otters were either from Elk River or were detected at least once at Elk
River (Table 7). Based on these various factors, there appears to be multiple, unrelated
social groups utilizing Elk River. Even when roaming individuals were removed and
relatedness values within Elk River became significantly greater than between sites, the
mean R value was still much lower than other sites (Figure 10). This could be possible if
more than one unrelated family group utilized Elk River at the same time. The Elk River
travels inland, providing more up-river habitat for river otters to utilize, which may
contribute to the patterns detected at the site. It is also possible that the area served as a
corridor between northern and southern Humboldt Bay.
Mark/recapture models using non-invasive sampling were an effective way to
estimate river otter abundance in the Humboldt Bay area. A two-month long sampling
regime during summer was sufficient for abundance estimates, although with exhaustive
sampling over 4.5 months, I was able to capture and genotype the majority of the
population and map movements of individual otters. Thus, I could evaluate structure and
social organization of these coastal river otters. Overall there was evidence for fine-
scale population structuring that was most likely a function of social family groups.
There was also support for some degree of bachelor group formation. These data suggest
that contemporary patterns of gene flow within Humboldt Bay may be geographically
54
restricted, creating genetic population structure. Understanding population social
structure, gene flow, and local behavioral adaptations is crucial for improving ecological
knowledge of river otters as well as properly managing this elusive species at a regional
level. The Humboldt Bay system highlights the fine-scale structuring and complex social
behavior river otters exhibit across their range. I would recommend these methods and
models to others interested in examining populations of river otters for management of
wetland ecosystems in a changing world.
55
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Appendix A. Universal Transverse Mercator (UTM) coordinates
of river otter latrine sites sampled 18 May-31 October
2008 in Humboldt Bay, California, USA.
Sampling Site Latrine Easting Northing
Little River flatrock 406709 4542435
Little River rock1 406717 4542432
Little River rock2 406613 4542504
Little River rock3 406706 4542435
Mad River pumpinglog 411905 4528818
Mad River pumpingden 411215 4529121
Mad River tyeecitylog1 404977 4530885
Mad River tyeecityslide 404979 4530877
Mad River tyeecitylog2 404937 4530954
Mad River boatramp 404876 4531475
MRS slide1 403271 4524579
MRS slide2 403351 4524666
MRS slide3 403366 4524735
MRS lamphere 403705 4512036
AMWS kloppslide 407536 4523432
AMWS kloppculvert 407971 4523263
AMWS grassyknoll 407297 4523914
AMWS hauserroad 407614 4523739
AMWS gallenrr1 407957 4523616
AMWS gallenrr2 407893 4523814
Woodley Island WIdock1 402196 4518122
Elk River tressel 399282 4512340
Elk River parkinglot1 399340 4512697
Elk River parkinglot2 399347 4512722
Elk River elkriver101 399448 4512328
HBNWR barn1 397800 4504677
HBNWR bridge1 397246 4504528
HBNWR bridge2 396868 4504640
HBNWR birdblind 397883 4504537
HBNWR HSdock 396739 4503672
HBNWR HSculvert 396479 4503993
65
Appendix B. Multi-locus microsatellite genotypes (N=41) for 6 loci and sex type derived
from DNA extracted from non-invasive sampling of river otters in Humboldt Bay,
California, USA, from 18 May-31 October 2008.
Sample Site Lut453 Lut733 Rio08 Lut701 Rio18 Lut604 Sex
ott3 Little 150 150 171 174 217 223 194 194 172 172 139 139 M
ott12 Little 146 150 171 174 217 217 194 198 156 172 139 139 F
ott15 Little 146 - 179 179 217 217 - - 156 172 139 139 U
ott35 Little 146 150 171 179 217 223 194 202 156 172 139 139 M
ott2 Mad 150 150 174 174 217 - 194 194 162 172 - - F
ott4 Mad 146 150 174 179 217 219 194 198 156 156 139 139 F
ott7 Mad 146 150 174 174 217 217 194 202 156 162 - 139 F
ott9 Mad 150 150 174 174 217 217 194 - 156 162 133 139 M
ott28 Mad 146 150 171 174 217 223 202 206 156 156 133 139 M
ott29 Mad 146 150 174 - - - 194 194 156 156 133 133 U
ott31 Mad 146 150 174 174 217 217 202 206 156 156 133 133 M
ott32 Mad 146 150 171 174 217 217 194 202 156 172 133 133 M
ott5 AMWS 142 150 171 174 217 219 194 194 156 162 133 139 F
ott10 AMWS 142 142 174 179 219 223 194 202 156 156 133 139 F
ott16 AMWS 146 150 171 179 219 219 194 194 156 156 133 139 M
ott19 AMWS 142 146 174 179 217 219 194 194 156 162 133 133 F
ott21 AMWS 150 150 171 179 219 219 194 194 156 156 133 139 F
ott27 AMWS 144 150 174 174 217 217 194 198 156 156 139 139 M
ott33 AMWS 142 150 174 179 217 219 194 202 156 162 133 139 M
ott36 AMWS 142 142 174 174 - - 194 - 156 - 133 139 U
ott37 AMWS 142 150 174 174 217 223 202 202 156 156 133 139 F
ott6 MRS 142 144 174 179 217 219 198 202 156 156 139 139 F
ott11 MRS 142 150 174 179 217 219 194 202 156 156 133 139 F
ott18 MRS 146 150 174 174 217 223 194 194 156 156 139 139 F
ott1 Elk 146 150 174 179 217 223 194 194 156 162 139 139 M
ott8 Elk 142 146 171 174 217 223 194 194 156 162 133 139 M
ott13 Elk 142 146 174 179 - - 194 202 172 - 133 139 F
ott14 Elk 146 150 171 174 223 223 194 194 156 172 139 139 M
ott17 Elk 146 150 171 174 223 223 194 194 156 156 139 139 M
ott20 Elk 144 150 174 174 217 217 198 202 156 156 139 139 M
ott23 Elk 142 146 174 179 - - 194 202 156 156 133 - M
ott24 Elk 146 146 171 174 223 223 194 194 156 156 139 139 M
ott26 Elk 146 150 171 174 217 223 194 194 156 162 133 133 M
ott30 Elk 146 146 171 174 217 - 194 194 156 162 133 133 F
ott34 Elk 144 - 174 174 217 217 198 202 156 162 - - M
ott39 Elk 142 150 174 174 217 217 194 198 156 162 139 139 M
ott40* Elk 146 150 171 179 219 219 194 202 156 156 139 139 M
ott41* Elk 146 150 171 179 219 219 194 202 156 156 139 169 F
ott22 HBNWR 150 150 171 179 217 - 194 198 156 172 133 139 M
ott25 HBNWR 150 150 171 174 217 223 194 206 156 172 139 139 M
ott38 HBNWR 150 150 171 171 223 223 194 194 156 156 139 139 F *Dyad with same multi-locus genotype, but different sex typing. “-“ indicates a non-confirmed allele.
66
Appendix C. River otters found at more than one sampling location, sex type, and dates
detected at all sites in Humboldt Bay, California, USA, from 18 May-31 October
2008. No roaming river otters were detected at Little River.
Animal
code Sex Mad AMWS MRS Woodley Elk River HBNWR
Ott16 M
10/27 7/1
Ott27 M
8/25
10/19
Ott33 M 10/20 8/6 9/29
Ott18 F
7/28
7/1
Ott1 M
5/27, 8/28 7/27, 9/15
Ott8 M
7/6, 7/7, 8/27 8/19, 9/8, 10/13
Ott20 M
8/25, 10/26
7/7, 9/23 8/19, 9/28, 10/5, 10/13 9/29
Ott40 M
9/8 10/26
67
Appendix D. Capture probability estimates (p), standard error (SE), and
95% confidence limits (CI) estimated from closed population
models as determined by program MARK for river otters sampled
in the Humboldt Bay region, California, USA, from 1 August-30
September, 2008. The top section is for capture histories
combining August and September (9 capture occasions); the
bottom section is only for September (5 capture occasions).
Capture occasion p SE Lower CI Upper CI
August and September
1 0.06 0.04 0.01 0.21
2 0.06 0.04 0.01 0.21
3 0.27 0.08 0.14 0.45
4 0.21 0.07 0.10 0.39
5 0.06 0.04 0.01 0.21
6 0.39 0.09 0.23 0.58
7 0.21 0.07 0.10 0.39
8 0.18 0.07 0.08 0.35
9 0.24 0.08 0.12 0.42
September only
1 0.06 0.04 0.01 0.22
2 0.40 0.11 0.22 0.62
3 0.22 0.08 0.10 0.41
4 0.19 0.08 0.08 0.38
5 0.25 0.09 0.12 0.45